{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dc6be60e",
   "metadata": {},
   "source": [
    "# 结论篇"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5222014f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge, Lasso, RidgeCV, LassoCV, LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6285d6bd",
   "metadata": {},
   "source": [
    "## 导入数据\n",
    "\n",
    "同时将数据标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3f1afc64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 9 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   longitude         20640 non-null  float64\n",
      " 1   latitude          20640 non-null  float64\n",
      " 2   housingMedianAge  20640 non-null  float64\n",
      " 3   totalRooms        20640 non-null  float64\n",
      " 4   totalBedrooms     20640 non-null  float64\n",
      " 5   population        20640 non-null  float64\n",
      " 6   households        20640 non-null  float64\n",
      " 7   medianIncome      20640 non-null  float64\n",
      " 8   medianHouseValue  20640 non-null  float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 1.4 MB\n"
     ]
    }
   ],
   "source": [
    "feature_names = [\n",
    "    'longitude',\n",
    "    'latitude',\n",
    "    'housingMedianAge',\n",
    "    'totalRooms',\n",
    "    'totalBedrooms',\n",
    "    'population',\n",
    "    'households',\n",
    "    'medianIncome',\n",
    "    'medianHouseValue'\n",
    "]\n",
    "df = pd.read_csv('cal_housing.data', header=None, names=feature_names)\n",
    "X = df.values[:, 0:8]\n",
    "y = df.values[:, 8]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, shuffle=True)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "full_scaler = StandardScaler()\n",
    "X_full = full_scaler.fit_transform(X)\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d11bf84",
   "metadata": {},
   "source": [
    "## 计算 Coef_Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c3b0b1a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas_lasso = np.logspace(-2, 6, 200)\n",
    "alphas_ridge = np.logspace(-2, 6, 200)\n",
    "\n",
    "lasso = Lasso()\n",
    "coef_path_lasso = []\n",
    "for alpha in alphas_lasso:\n",
    "    lasso.set_params(alpha=alpha)\n",
    "    lasso.fit(X_full, y)\n",
    "    coef_path_lasso.append(lasso.coef_)\n",
    "coef_path_lasso = np.array(coef_path_lasso)\n",
    "\n",
    "ridge = Ridge()\n",
    "coef_path_ridge = []\n",
    "for alpha in alphas_ridge:\n",
    "    ridge.set_params(alpha=alpha)\n",
    "    ridge.fit(X_full, y)\n",
    "    coef_path_ridge.append(ridge.coef_)\n",
    "coef_path_ridge = np.array(coef_path_ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aa88af09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-85796.54450207, -90939.54530375,  14552.38249241, ...,\n",
       "        -43638.4141099 ,  18469.20353202,  76463.11188679],\n",
       "       [-85796.53419096, -90939.53531105,  14552.38308621, ...,\n",
       "        -43638.40778795,  18469.19905649,  76463.10831093],\n",
       "       [-85796.52287912, -90939.52434791,  14552.38373772, ...,\n",
       "        -43638.40084762,  18469.19413336,  76463.1043905 ],\n",
       "       ...,\n",
       "       [    -0.        ,     -0.        ,      0.        , ...,\n",
       "            -0.        ,      0.        ,      0.        ],\n",
       "       [    -0.        ,     -0.        ,      0.        , ...,\n",
       "            -0.        ,      0.        ,      0.        ],\n",
       "       [    -0.        ,     -0.        ,      0.        , ...,\n",
       "            -0.        ,      0.        ,      0.        ]],\n",
       "      shape=(200, 8))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef_path_lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d23b0a7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(alphas_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a757cc43",
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso_df = pd.DataFrame(coef_path_lasso, alphas_lasso, columns=feature_names[0:8])\n",
    "ridge_df = pd.DataFrame(coef_path_ridge, alphas_ridge, columns=feature_names[0:8])\n",
    "lasso_df.to_excel('lasso_coef_path.xlsx')\n",
    "ridge_df.to_excel('ridge_coef_path.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "860f33dc",
   "metadata": {},
   "source": [
    "## 画图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8c307aa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(alphas_lasso, coef_path_lasso)\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Alpha')\n",
    "plt.ylabel('Coefficient Value')\n",
    "plt.title('Lasso Coefficient Path')\n",
    "plt.legend(feature_names, ncol=1, loc=(1.1, 0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2d02786c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(alphas_ridge, coef_path_ridge)\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Alpha')\n",
    "plt.ylabel('Coefficient Value')\n",
    "plt.title('Ridge Coefficient Path')\n",
    "plt.legend(feature_names, ncol=1, loc=(1.1, 0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4ecfd0e",
   "metadata": {},
   "source": [
    "这个图表现了各项回归系数由于惩罚项归零的过程。  \n",
    "一般来说，系数绝对值越大，表明该特征决定性越高。  \n",
    "图中可以看出，最终所有回归系数都被压缩，  \n",
    "但被压缩的顺序不一样，程度不一样。  \n",
    "\n",
    "---\n",
    "\n",
    "在 Lasso 回归中，  \n",
    "`totalRooms` `households` 最先被压缩，  \n",
    "`medianIncome` 特征最后被压缩\n",
    "\n",
    "---\n",
    "\n",
    "Ridge 回归中，  \n",
    "`totalBedrooms` `totalRooms` 最先被压缩，  \n",
    "`medianIncome` 特征最后被压缩\n",
    "\n",
    "我们可以大致的出结论，`medianIncome` 特征影响因素较大"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bb2cbfa",
   "metadata": {},
   "source": [
    "### :fire:特殊现象\n",
    "\n",
    "此外，我们还观察到了 `totalRooms` 的系数符号出现了变化的情况，\n",
    "\n",
    "可能是 **多重共线性（Multicollinearity）** 的影响  \n",
    "当特征之间存在高度相关性时，Ridge 回归可能会导致系数符号改变：\n",
    "\n",
    "**原理** ：多重共线性使得参数估计的方差增大，普通最小二乘法（OLS）的解不稳定。Ridge 回归通过引入偏差来降低方差，但在强相关特征之间，可能会通过调整某些系数的符号来平衡整体损失。  \n",
    "**示例** ：假设两个特征 $X_1$ 和 $X_2$ 高度正相关，但真实影响方向相反（如 $X_1$ 为正效应，$X_2$ 为负效应）。OLS 可能会因共线性而错误估计两者均为正效应。Ridge 回归在压缩过程中可能会 “纠正” 这种错误，将其中一个系数符号变为负。\n",
    "\n",
    "下面进行相关性分析，绘制相关性热力图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1df8423c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix = df.iloc[:, 0:8].corr()\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.imshow(correlation_matrix, cmap='coolwarm', vmin=-1, vmax=1)\n",
    "\n",
    "plt.xticks(np.arange(8), labels=feature_names[0:8], rotation=45, rotation_mode='anchor', ha='right')\n",
    "plt.yticks(np.arange(8), labels=feature_names[0:8])\n",
    "plt.title('Correlation coefficient heat map')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c28bf9ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housingMedianAge</th>\n",
       "      <th>totalRooms</th>\n",
       "      <th>totalBedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>medianIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.924664</td>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.044568</td>\n",
       "      <td>0.068378</td>\n",
       "      <td>0.099773</td>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.015176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>-0.924664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.066318</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.079809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housingMedianAge</th>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>-0.320485</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>-0.119034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totalRooms</th>\n",
       "      <td>0.044568</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.929893</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.198050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totalBedrooms</th>\n",
       "      <td>0.068378</td>\n",
       "      <td>-0.066318</td>\n",
       "      <td>-0.320485</td>\n",
       "      <td>0.929893</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.878026</td>\n",
       "      <td>0.979829</td>\n",
       "      <td>-0.008093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>0.099773</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.878026</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>0.004834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.979829</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medianIncome</th>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>-0.008093</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  longitude  latitude  housingMedianAge  totalRooms  \\\n",
       "longitude          1.000000 -0.924664         -0.108197    0.044568   \n",
       "latitude          -0.924664  1.000000          0.011173   -0.036100   \n",
       "housingMedianAge  -0.108197  0.011173          1.000000   -0.361262   \n",
       "totalRooms         0.044568 -0.036100         -0.361262    1.000000   \n",
       "totalBedrooms      0.068378 -0.066318         -0.320485    0.929893   \n",
       "population         0.099773 -0.108785         -0.296244    0.857126   \n",
       "households         0.055310 -0.071035         -0.302916    0.918484   \n",
       "medianIncome      -0.015176 -0.079809         -0.119034    0.198050   \n",
       "\n",
       "                  totalBedrooms  population  households  medianIncome  \n",
       "longitude              0.068378    0.099773    0.055310     -0.015176  \n",
       "latitude              -0.066318   -0.108785   -0.071035     -0.079809  \n",
       "housingMedianAge      -0.320485   -0.296244   -0.302916     -0.119034  \n",
       "totalRooms             0.929893    0.857126    0.918484      0.198050  \n",
       "totalBedrooms          1.000000    0.878026    0.979829     -0.008093  \n",
       "population             0.878026    1.000000    0.907222      0.004834  \n",
       "households             0.979829    0.907222    1.000000      0.013033  \n",
       "medianIncome          -0.008093    0.004834    0.013033      1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_matrix.to_excel(\"Correlation coefficient heat map.xlsx\")\n",
    "correlation_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aab60017",
   "metadata": {},
   "source": [
    "## 导出最优模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d3c0014a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cv 会自动选择最优参数\n",
    "lasso_cv = LassoCV(alphas=alphas_lasso, cv=5)\n",
    "lasso_cv.fit(X_train, y_train)\n",
    "best_alpha_lasso = lasso_cv.alpha_\n",
    "\n",
    "ridge_cv = RidgeCV(alphas=alphas_ridge, cv=5)\n",
    "ridge_cv.fit(X_train, y_train)\n",
    "best_alpha_ridge = ridge_cv.alpha_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "367dbb29",
   "metadata": {},
   "source": [
    "自动选择最优参数时，是用的 K折交叉验证 或 留一法，也就是说其中会自动将整个数据集划分训练集和测试集。  \n",
    "下面评价数据的地方实际上继续用K折交叉验证更全面一些，但我没写 K折交叉验证，因为太懒了（）  \n",
    "\n",
    "~~周三尝试了。。。~~  \n",
    "~~有个奇怪的现象，直接把整个数据扔给 LassoCV 之后，它就建议把 alpha 设为 0 了~~  \n",
    "~~我觉得这个属于偶然现象，跟咱们结论不相符，干脆不改了，也规避了这个问题~~  \n",
    "~~也不知道这个算不算学术造假（bushi）~~  \n",
    "\n",
    "破案了，全体数据忘记标准化了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "427f0d47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housingMedianAge</th>\n",
       "      <th>totalRooms</th>\n",
       "      <th>totalBedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>medianIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear</th>\n",
       "      <td>-85503.218153</td>\n",
       "      <td>-90698.848803</td>\n",
       "      <td>14905.906448</td>\n",
       "      <td>-17805.418501</td>\n",
       "      <td>48712.343341</td>\n",
       "      <td>-43766.491159</td>\n",
       "      <td>17654.500659</td>\n",
       "      <td>77194.699466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso</th>\n",
       "      <td>-85069.782520</td>\n",
       "      <td>-90279.549219</td>\n",
       "      <td>14928.478341</td>\n",
       "      <td>-17027.177572</td>\n",
       "      <td>47914.974901</td>\n",
       "      <td>-43493.225232</td>\n",
       "      <td>17444.650562</td>\n",
       "      <td>77035.400171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge</th>\n",
       "      <td>-84196.815185</td>\n",
       "      <td>-89405.014804</td>\n",
       "      <td>15022.168226</td>\n",
       "      <td>-17391.316533</td>\n",
       "      <td>47180.356679</td>\n",
       "      <td>-43602.055912</td>\n",
       "      <td>18661.584228</td>\n",
       "      <td>77143.690431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           longitude      latitude  housingMedianAge    totalRooms  \\\n",
       "Linear -85503.218153 -90698.848803      14905.906448 -17805.418501   \n",
       "Lasso  -85069.782520 -90279.549219      14928.478341 -17027.177572   \n",
       "Ridge  -84196.815185 -89405.014804      15022.168226 -17391.316533   \n",
       "\n",
       "        totalBedrooms    population    households  medianIncome  \n",
       "Linear   48712.343341 -43766.491159  17654.500659  77194.699466  \n",
       "Lasso    47914.974901 -43493.225232  17444.650562  77035.400171  \n",
       "Ridge    47180.356679 -43602.055912  18661.584228  77143.690431  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso = Lasso(alpha=best_alpha_lasso)\n",
    "lasso.fit(X_train, y_train)\n",
    "\n",
    "ridge = Ridge(alpha=best_alpha_ridge)\n",
    "ridge.fit(X_train, y_train)\n",
    "\n",
    "linear = LinearRegression()\n",
    "linear.fit(X_train, y_train)\n",
    "\n",
    "pd.DataFrame([linear.coef_.reshape([8]), lasso.coef_.reshape([8]), ridge.coef_.reshape([8])],\n",
    "             ['Linear', 'Lasso', 'Ridge'], columns=feature_names[0:8])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "183b8d2e",
   "metadata": {},
   "source": [
    "## 评估数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7e62d294",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_lasso_train = lasso_cv.predict(X_train)\n",
    "y_pred_lasso_test = lasso_cv.predict(X_test)\n",
    "mse_lasso_train = mean_squared_error(y_train, y_pred_lasso_train)\n",
    "mse_lasso_test = mean_squared_error(y_test, y_pred_lasso_test)\n",
    "r2_lasso_train = r2_score(y_train, y_pred_lasso_train)\n",
    "r2_lasso_test = r2_score(y_test, y_pred_lasso_test)\n",
    "\n",
    "y_pred_ridge_train = ridge_cv.predict(X_train)\n",
    "y_pred_ridge_test = ridge_cv.predict(X_test)\n",
    "mse_ridge_train = mean_squared_error(y_train, y_pred_ridge_train)\n",
    "mse_ridge_test = mean_squared_error(y_test, y_pred_ridge_test)\n",
    "r2_ridge_train = r2_score(y_train, y_pred_ridge_train)\n",
    "r2_ridge_test = r2_score(y_test, y_pred_ridge_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d4298b99",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_linear_train = linear.predict(X_train)\n",
    "y_pred_linear_test = linear.predict(X_test)\n",
    "mse_linear_train = mean_squared_error(y_train, y_pred_linear_train)\n",
    "mse_linear_test = mean_squared_error(y_test, y_pred_linear_test)\n",
    "r2_linear_train = r2_score(y_train, y_pred_linear_train)\n",
    "r2_linear_test = r2_score(y_test, y_pred_linear_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "358fcae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tag</th>\n",
       "      <th>linear</th>\n",
       "      <th>Lasso</th>\n",
       "      <th>Ridge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.150405e+01</td>\n",
       "      <td>1.644676e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train MSE</td>\n",
       "      <td>4.811134e+09</td>\n",
       "      <td>4.811262e+09</td>\n",
       "      <td>4.811429e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test MSE</td>\n",
       "      <td>4.918556e+09</td>\n",
       "      <td>4.918389e+09</td>\n",
       "      <td>4.918482e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train R2</td>\n",
       "      <td>6.400948e-01</td>\n",
       "      <td>6.400853e-01</td>\n",
       "      <td>6.400728e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>test R2</td>\n",
       "      <td>6.246549e-01</td>\n",
       "      <td>6.246676e-01</td>\n",
       "      <td>6.246605e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Tag        linear         Lasso         Ridge\n",
       "0      Alpha  0.000000e+00  4.150405e+01  1.644676e+01\n",
       "1  train MSE  4.811134e+09  4.811262e+09  4.811429e+09\n",
       "2   test MSE  4.918556e+09  4.918389e+09  4.918482e+09\n",
       "3   train R2  6.400948e-01  6.400853e-01  6.400728e-01\n",
       "4    test R2  6.246549e-01  6.246676e-01  6.246605e-01"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = ['Tag', 'linear', 'Lasso', 'Ridge']\n",
    "output = [\n",
    "    ['Alpha', 0, best_alpha_lasso, best_alpha_ridge],\n",
    "    ['train MSE', mse_linear_train, mse_lasso_train, mse_ridge_train],\n",
    "    ['test MSE', mse_linear_test, mse_lasso_test, mse_ridge_test],\n",
    "    ['train R2', r2_linear_train, r2_lasso_train, r2_ridge_train],\n",
    "    ['test R2', r2_linear_test, r2_lasso_test, r2_ridge_test]\n",
    "]\n",
    "pd.DataFrame(output, columns=names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22478eb8",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "下面是手动把 $\\lambda$ 调到 1000 的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "38b6f5e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housingMedianAge</th>\n",
       "      <th>totalRooms</th>\n",
       "      <th>totalBedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>medianIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear</th>\n",
       "      <td>-85503.218153</td>\n",
       "      <td>-90698.848803</td>\n",
       "      <td>14905.906448</td>\n",
       "      <td>-17805.418501</td>\n",
       "      <td>48712.343341</td>\n",
       "      <td>-43766.491159</td>\n",
       "      <td>17654.500659</td>\n",
       "      <td>77194.699466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso</th>\n",
       "      <td>-74941.959033</td>\n",
       "      <td>-80428.485623</td>\n",
       "      <td>15446.644250</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>30765.914155</td>\n",
       "      <td>-36830.364872</td>\n",
       "      <td>11909.944183</td>\n",
       "      <td>73577.396304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge</th>\n",
       "      <td>-45942.617027</td>\n",
       "      <td>-50991.436210</td>\n",
       "      <td>17948.460052</td>\n",
       "      <td>-5601.688902</td>\n",
       "      <td>22458.129238</td>\n",
       "      <td>-30832.707322</td>\n",
       "      <td>20719.995256</td>\n",
       "      <td>74043.264024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           longitude      latitude  housingMedianAge    totalRooms  \\\n",
       "Linear -85503.218153 -90698.848803      14905.906448 -17805.418501   \n",
       "Lasso  -74941.959033 -80428.485623      15446.644250     -0.000000   \n",
       "Ridge  -45942.617027 -50991.436210      17948.460052  -5601.688902   \n",
       "\n",
       "        totalBedrooms    population    households  medianIncome  \n",
       "Linear   48712.343341 -43766.491159  17654.500659  77194.699466  \n",
       "Lasso    30765.914155 -36830.364872  11909.944183  73577.396304  \n",
       "Ridge    22458.129238 -30832.707322  20719.995256  74043.264024  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso = Lasso(alpha=1000)\n",
    "lasso.fit(X_train, y_train)\n",
    "\n",
    "ridge = Ridge(alpha=1000)\n",
    "ridge.fit(X_train, y_train)\n",
    "\n",
    "linear = LinearRegression()\n",
    "linear.fit(X_train, y_train)\n",
    "\n",
    "pd.DataFrame([linear.coef_.reshape([8]), lasso.coef_.reshape([8]), ridge.coef_.reshape([8])],\n",
    "             ['Linear', 'Lasso', 'Ridge'], columns=feature_names[0:8])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98c208b1",
   "metadata": {},
   "source": [
    "## 评估数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6af0f774",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_lasso_train = lasso_cv.predict(X_train)\n",
    "y_pred_lasso_test = lasso_cv.predict(X_test)\n",
    "mse_lasso_train = mean_squared_error(y_train, y_pred_lasso_train)\n",
    "mse_lasso_test = mean_squared_error(y_test, y_pred_lasso_test)\n",
    "r2_lasso_train = r2_score(y_train, y_pred_lasso_train)\n",
    "r2_lasso_test = r2_score(y_test, y_pred_lasso_test)\n",
    "\n",
    "y_pred_ridge_train = ridge_cv.predict(X_train)\n",
    "y_pred_ridge_test = ridge_cv.predict(X_test)\n",
    "mse_ridge_train = mean_squared_error(y_train, y_pred_ridge_train)\n",
    "mse_ridge_test = mean_squared_error(y_test, y_pred_ridge_test)\n",
    "r2_ridge_train = r2_score(y_train, y_pred_ridge_train)\n",
    "r2_ridge_test = r2_score(y_test, y_pred_ridge_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "aa8a061e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_linear_train = linear.predict(X_train)\n",
    "y_pred_linear_test = linear.predict(X_test)\n",
    "mse_linear_train = mean_squared_error(y_train, y_pred_linear_train)\n",
    "mse_linear_test = mean_squared_error(y_test, y_pred_linear_test)\n",
    "r2_linear_train = r2_score(y_train, y_pred_linear_train)\n",
    "r2_linear_test = r2_score(y_test, y_pred_linear_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c1fadd22",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tag</th>\n",
       "      <th>linear</th>\n",
       "      <th>Lasso</th>\n",
       "      <th>Ridge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.150405e+01</td>\n",
       "      <td>1.644676e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train MSE</td>\n",
       "      <td>4.811134e+09</td>\n",
       "      <td>4.811262e+09</td>\n",
       "      <td>4.811429e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test MSE</td>\n",
       "      <td>4.918556e+09</td>\n",
       "      <td>4.918389e+09</td>\n",
       "      <td>4.918482e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train R2</td>\n",
       "      <td>6.400948e-01</td>\n",
       "      <td>6.400853e-01</td>\n",
       "      <td>6.400728e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>test R2</td>\n",
       "      <td>6.246549e-01</td>\n",
       "      <td>6.246676e-01</td>\n",
       "      <td>6.246605e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Tag        linear         Lasso         Ridge\n",
       "0      Alpha  0.000000e+00  4.150405e+01  1.644676e+01\n",
       "1  train MSE  4.811134e+09  4.811262e+09  4.811429e+09\n",
       "2   test MSE  4.918556e+09  4.918389e+09  4.918482e+09\n",
       "3   train R2  6.400948e-01  6.400853e-01  6.400728e-01\n",
       "4    test R2  6.246549e-01  6.246676e-01  6.246605e-01"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = ['Tag', 'linear', 'Lasso', 'Ridge']\n",
    "output = [\n",
    "    ['Alpha', 0, best_alpha_lasso, best_alpha_ridge],\n",
    "    ['train MSE', mse_linear_train, mse_lasso_train, mse_ridge_train],\n",
    "    ['test MSE', mse_linear_test, mse_lasso_test, mse_ridge_test],\n",
    "    ['train R2', r2_linear_train, r2_lasso_train, r2_ridge_train],\n",
    "    ['test R2', r2_linear_test, r2_lasso_test, r2_ridge_test]\n",
    "]\n",
    "pd.DataFrame(output, columns=names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dec16baf",
   "metadata": {},
   "source": [
    "我们看到，这个把特征压缩后，实际上对准确率并没有多大帮助，  \n",
    "这是因为数据集共线性性质太强了，并且，数据集噪声本身实际上也没有对线性模型造成太大影响  \n",
    "导致 Lasso 并没有什么用。"
   ]
  }
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